BigCommerce is an ecommerce platform that provides a hosted shopping cart for online merchants, inventory and order management features, payment integration, and marketing tools.

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

BigCommerce provides several APIs that let developers retrieve customer, order, storefront, catalog, payment, and other information stored in the platform. For example, to get information about a particular product, you would call GET /catalog/products/{product_id}.

Here's an example of the kind of response you might see with a query like the one above.

{
  "data": {
    "id": 174,
    "name": "1L Le Parfait Jar",
    "type": "physical",
    "sku": "",
    "description": "
Le Parfait Jars are awesome. You should buy some.
",
    "weight": 1,
    "width": 0,
    "depth": 0,
    "height": 0,
    "price": 7.95,
    "cost_price": 0,
    "retail_price": 10,
    "sale_price": 0,
    "map_price": 0,
    "tax_class_id": 0,
    "product_tax_code": "",
    "calculated_price": 7.95,
    "categories": [
      23,
      21
    ],
    "brand_id": 36,
    "option_set_id": null,
    "option_set_display": "right",
    "inventory_level": 0,
    "inventory_warning_level": 0,
    "inventory_tracking": "none",
    "reviews_rating_sum": 0,
    "reviews_count": 0,
    "total_sold": 7,
    "fixed_cost_shipping_price": 0,
    "is_free_shipping": false,
    "is_visible": true,
    "is_featured": false,
    "related_products": [
      -1
    ],
    "warranty": "",
    "bin_picking_number": "",
    "layout_file": "product.html",
    "upc": "",
    "mpn": "",
    "gtin": "",
    "search_keywords": "jar, glass",
    "availability": "available",
    "availability_description": "",
    "gift_wrapping_options_type": "any",
    "gift_wrapping_options_list": [],
    "sort_order": 0,
    "condition": "New",
    "is_condition_shown": false,
    "order_quantity_minimum": 0,
    "order_quantity_maximum": 0,
    "page_title": "",
    "meta_keywords": [],
    "meta_description": "",
    "date_created": "2018-08-15T14:48:46+00:00",
    "date_modified": "2018-09-05T20:42:07+00:00",
    "view_count": 10,
    "preorder_release_date": null,
    "preorder_message": "",
    "is_preorder_only": false,
    "is_price_hidden": false,
    "price_hidden_label": "",
    "custom_url": {
      "url": "/all/1l-le-parfait-jar/",
      "is_customized": true
    },
    "base_variant_id": 345,
    "open_graph_type": "product",
    "open_graph_title": "",
    "open_graph_description": "",
    "open_graph_use_meta_description": true,
    "open_graph_use_product_name": true,
    "open_graph_use_image": true
  },
  "meta": {}
}

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. BigCommerce's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool to upload the files to your awaiting datasets, adding the correct schema and data type information along the way. The bq load command is your friend here. You can find the syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, BigCommerce's API results include fields like date_created that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

BigQuery is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.